Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy  

作　　者：Sirirak Ditcharoen Panmanas Sirisomboon Khwantri Saengprachatanarug Arthit Phuphaphud Ronnarit Rittiron Anupun Terdwongworakul Chayuttapong Malai Chirawan Saenphon Lalita Panduangnate Jetsada Posom 

机构地区：[1]Department of Agricultural Engineering,Faculty of Engineering,Khon Kaen University,Khon Kaen 40002,Thailand [2]Department of Agricultural Engineering,School of Engineering,King Mongkut's Institute of Technology Ladkrabang,Bangkok 10520,Thailand [3]Department of Food Engineering,Faculty of Engineering at Kamphaengsaen,Kasetsart University,Nakhon Pathom 73140,Thailand [4]Department of Agricultural Engineering,Faculty of Engineering at Kamphaeng Saen,Kasetsart University,Kamphaeng Saen,Nakhon Pathom 73140,Thailand

出　　处：《Artificial Intelligence in Agriculture》2023年第1期35-43,共9页农业人工智能（英文）

基　　金：supported by Research and Graduate Studies,Khon Kaen University,Thailand;Research Fund for Supporting Lecturer to Admit High Potential Student to Study and Research on His Expert Program Year 2021 from Graduate School,Khon Kaen University,Thailand;the Agricultural Research Development Agency(Public Organisation)[grant number CRP6405031580]。

摘　　要：The maturity state of durian fruit is a key indicator of quality before trading.This research aims to improve the near-infrared(NIR)model for classifying the maturity stage of durian fruit using a completely non-destructive measurement.Both NIR spectrometers were investigated:the short wavelength NIR(SWNIR)ranging from 450 to 1000 nm and long wavelength NIR(LWNIR)ranging from 860 to 1750 nm.The samples collected for experimentation consisted of four stages:immaturity,prematurity,maturity,and ripe.Each fruit was scanned at the rind position on the main fertile lobe(header,middle,and tail)and stem.The classification models were developed using three supervised machine learning algorithms:linear discriminant analysis(LDA),support vector machine(SVM),and K-Nearest neighbours(KNN).The analysis results revealed that the use of durian rind spectra only obtained between 83.15%and 88.04%accuracy for the LWNIR spectrometer,while the SWNIR spectrometer provided 64.73 to 93.77%accuracy.The performance of model increases when developing with combination between rind and stem spectra.The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency,exhibiting 97.28%and 100%accuracy for LWNIR and SWNIR spectrometers,respectively.The LDAmodelis therefore recommended for obtaining spectra from smoothingmoving average(MA)+baseline of rind position and when used in combination with the MA+standard normal variance(SNV)of stem spectra.The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage.This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.

关 键 词：CLASSIFICATION Durian fruit Maturity stage Near-infrared spectroscopy Non-destructive method 

分 类 号：TN21[电子电信—物理电子学]